Over-the-air computation is a communication-efficient solution for federated learning (FL). In
such a system, iterative procedure is performed: Local gradient of private loss function is …

In federated learning (FL), a number of devices train their local models and upload the
corresponding parameters or gradients to the base station (BS) to update the global model …

Federated learning (FL) as a promising edge-learning framework can effectively address the
latency and privacy issues by featuring distributed learning at the devices and model …

Federated learning (FL) provides a privacy-preserving approach to realizing networked
intelligence. However, the performance of FL is often constrained by the limited …

The emerging field of federated learning (FL) provides great potential for edge intelligence
while protecting data privacy. However, as the system grows in scale or becomes more …

Federated learning (FL) has emerged as an appealing machine learning approach to deal
with massive raw data generated at multiple mobile devices, which needs to aggregate the …

Federated learning (FL), as an emerging distributed machine learning paradigm, allows a
mass of edge devices to collaboratively train a global model while preserving privacy. In this …

Federated learning (FL) is an emerging technique that enables privacy-preserving
distributed learning. Most related works focus on centralized FL, which leverages the …

Owing to their fast convergence, second-order Newton-type learning methods have recently
received attention in the federated learning (FL) setting. However, current solutions are …

Federated learning (FL) is a framework of large-scale distributed learning with user privacy
protection through local training and global aggregation. However, FL may suffer from …